11 research outputs found
The Bernstein Function: A Unifying Framework of Nonconvex Penalization in Sparse Estimation
In this paper we study nonconvex penalization using Bernstein functions.
Since the Bernstein function is concave and nonsmooth at the origin, it can
induce a class of nonconvex functions for high-dimensional sparse estimation
problems. We derive a threshold function based on the Bernstein penalty and
give its mathematical properties in sparsity modeling. We show that a
coordinate descent algorithm is especially appropriate for penalized regression
problems with the Bernstein penalty. Additionally, we prove that the Bernstein
function can be defined as the concave conjugate of a -divergence and
develop a conjugate maximization algorithm for finding the sparse solution.
Finally, we particularly exemplify a family of Bernstein nonconvex penalties
based on a generalized Gamma measure and conduct empirical analysis for this
family
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described